{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Training Prediction Models Directly Within PostgreSQL Using XGBoost EvaDB\n",
        "In this tutorial, we'll harness EvaDB's model training capabilities to predict home rental prices, showcasing how EvaDB seamlessly integrates AI into your PostgreSQL database.\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/georgia-tech-db/eva/blob/staging/tutorials/19-employee-classification-prediction.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\"/> Run on Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/georgia-tech-db/eva/blob/staging/tutorials/19-employee-classification-prediction.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\"/> View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/georgia-tech-db/eva/raw/staging/tutorials/19-employee-classification-prediction.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /> Download notebook</a>\n",
        "  </td>\n",
        "</table><br><br>"
      ],
      "metadata": {
        "id": "4o38TEFPWmZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!apt -qq install postgresql\n",
        "!service postgresql start"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LgHQ_6J7sPs",
        "outputId": "7a52b8e1-a36b-4b41-b80e-b7b2be7d9a18"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "postgresql is already the newest version (14+238).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 19 not upgraded.\n",
            " * Starting PostgreSQL 14 database server\n",
            "   ...done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "nsyZe8PmZYZ7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install and Launch the PostgreSQL Server\n",
        "\n",
        "To kick things off, we'll start by setting up the PostgreSQL database backend. If you already have a PostgreSQL server up and running, you can skip this step and proceed directly to [installing EvaDB](#install-evadb)."
      ],
      "metadata": {
        "id": "i1sYj0bRZf6m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create User and Database"
      ],
      "metadata": {
        "id": "FORYt3tWZpM1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo -u postgres psql -c \"CREATE USER eva WITH SUPERUSER PASSWORD 'password'\"\n",
        "!sudo -u postgres psql -c \"CREATE DATABASE evadb\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngJzr2B77zF2",
        "outputId": "309c5b37-bef4-4c10-9401-aff0fc22a92d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ERROR:  role \"eva\" already exists\n",
            "ERROR:  database \"evadb\" already exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prettify  Output"
      ],
      "metadata": {
        "id": "uQh_rSIGZvNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from IPython.core.display import display, HTML\n",
        "def pretty_print(df):\n",
        "    return display(HTML( df.to_html().replace(\"\\\\n\",\"<br>\")))"
      ],
      "metadata": {
        "id": "T8sDqf9870dh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installing EvaDB and XGBoost dependencies\n",
        "<a id='install_evadb'></a>\n",
        "We install EvaDB along with the necessary PostgreSQL and XGBoost dependencies."
      ],
      "metadata": {
        "id": "cgkNFXEnZ1jI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install --quiet \"evadb[postgres,xgboost] @ git+https://github.com/georgia-tech-db/evadb.git@703dc9460e499a693ee83bfefe9fe49918499159\"\n",
        "\n",
        "import evadb\n",
        "cursor = evadb.connect().cursor()"
      ],
      "metadata": {
        "id": "Kf2xgkvr76bZ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load data into PostgresSQL"
      ],
      "metadata": {
        "id": "gtzlHzn0aC3F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up a Data Source in EvaDB\n",
        "To establish a direct connection between EvaDB and underlying database systems such as PostgreSQL, we will create a data source. This process entails supplying EvaDB with the connection credentials for the active PostgreSQL server."
      ],
      "metadata": {
        "id": "eHUbzlvXaZIC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "params = {\n",
        "    \"user\": \"eva\",\n",
        "    \"password\": \"password\",\n",
        "    \"host\": \"localhost\",\n",
        "    \"port\": \"5432\",\n",
        "    \"database\": \"evadb\",\n",
        "}\n",
        "query = f\"CREATE DATABASE postgres_data WITH ENGINE = 'postgres', PARAMETERS = {params};\"\n",
        "cursor.query(query).df()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "sE_Q8-mM795g",
        "outputId": "302c4336-a78c-4072-a3ce-0c904e3d0051"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "10-27-2023 05:16:54 ERROR [plan_executor:plan_executor.py:execute_plan:0179] postgres_data already exists.\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\", line 175, in execute_plan\n",
            "    yield from output\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/create_database_executor.py\", line 42, in exec\n",
            "    raise ExecutorError(f\"{self.node.database_name} already exists.\")\n",
            "evadb.executor.executor_utils.ExecutorError: postgres_data already exists.\n",
            "ERROR:evadb.utils.logging_manager:postgres_data already exists.\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\", line 175, in execute_plan\n",
            "    yield from output\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/create_database_executor.py\", line 42, in exec\n",
            "    raise ExecutorError(f\"{self.node.database_name} already exists.\")\n",
            "evadb.executor.executor_utils.ExecutorError: postgres_data already exists.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ExecutorError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mExecutorError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\u001b[0m in \u001b[0;36mexecute_plan\u001b[0;34m(self, do_not_raise_exceptions, do_not_print_exceptions)\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/create_database_executor.py\u001b[0m in \u001b[0;36mexec\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mExecutorError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.node.database_name} already exists.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mExecutorError\u001b[0m: postgres_data already exists.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mExecutorError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-28b234e6d6df>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m }\n\u001b[1;32m      8\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"CREATE DATABASE postgres_data WITH ENGINE = 'postgres', PARAMETERS = {params};\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/interfaces/relational/relation.py\u001b[0m in \u001b[0;36mdf\u001b[0;34m(self, drop_alias)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;36m2\u001b[0m      \u001b[0;36m5\u001b[0m     \u001b[0;36m6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \"\"\"\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop_alias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_alias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"relation execute failed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/interfaces/relational/relation.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, drop_alias)\u001b[0m\n\u001b[1;32m    139\u001b[0m             \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT * FROM MyTable;\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \"\"\"\n\u001b[0;32m--> 141\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecute_statement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evadb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query_node\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m         \u001b[0;31m# TODO: this is a dirty implementation. Ideally this should be done in the final projection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdrop_alias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/server/command_handler.py\u001b[0m in \u001b[0;36mexecute_statement\u001b[0;34m(evadb, stmt, do_not_raise_exceptions, do_not_print_exceptions, **kwargs)\u001b[0m\n\u001b[1;32m     51\u001b[0m     )\n\u001b[1;32m     52\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mbatch_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mBatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\u001b[0m in \u001b[0;36mexecute_plan\u001b[0;34m(self, do_not_raise_exceptions, do_not_print_exceptions)\u001b[0m\n\u001b[1;32m    178\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdo_not_print_exceptions\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mExecutorError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mExecutorError\u001b[0m: postgres_data already exists."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Classification Models using EVADB\n",
        "\n",
        "Next, we employ EvaDB to facilitate the training of Classification ML models, which will enable us to predict `leave_or_not` i.e. a variable depicting whether an employee will leave the current company or not based on several parameters."
      ],
      "metadata": {
        "id": "ZDVPDGqjfrch"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading Employee Data from CSV into PostgreSQL\n",
        "\n",
        "In this step, we will import the [Employee Data](https://www.kaggle.com/datasets/tawfikelmetwally/employee-dataset) dataset into our PostgreSQL database. If you already have the data stored in PostgreSQL and are ready to proceed with the prediction model training, feel free to skip this section and head directly to the [model training process](#train-the-prediction-model)."
      ],
      "metadata": {
        "id": "ODq5QPC3gp5U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p content\n",
        "!wget -nc -O /content/Employee.csv https://drive.google.com/file/d/1R4ij5Ww6bOGwLJrbBStzcaPRhAJ-fn72/view?usp=share_link"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlJ4adTuiDUF",
        "outputId": "e99e9500-dec6-4543-ca32-c113f488d941"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-27 04:16:45--  https://drive.google.com/file/d/1R4ij5Ww6bOGwLJrbBStzcaPRhAJ-fn72/view?usp=share_link\n",
            "Resolving drive.google.com (drive.google.com)... 172.253.123.139, 172.253.123.101, 172.253.123.138, ...\n",
            "Connecting to drive.google.com (drive.google.com)|172.253.123.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘/content/Employee.csv’\n",
            "\n",
            "/content/Employee.c     [ <=>                ]  81.89K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2023-10-27 04:16:45 (60.3 MB/s) - ‘/content/Employee.csv’ saved [83856]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cursor.query(\"\"\"\n",
        "  USE postgres_data {\n",
        "    CREATE TABLE IF NOT EXISTS employee_data (\n",
        "      education VARCHAR(128),\n",
        "      joining_year INTEGER,\n",
        "      city VARCHAR(128),\n",
        "      payment_tier INTEGER,\n",
        "      age INTEGER,\n",
        "      gender VARCHAR(128),\n",
        "      ever_benched VARCHAR(128),\n",
        "      experience_in_current_domain INTEGER,\n",
        "      leave_or_not INTEGER\n",
        "    )\n",
        "  }\n",
        "\"\"\").df()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "g_KW1uc2iNdv",
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        {
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              "    status\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cursor.query(\"\"\"\n",
        "  USE postgres_data {\n",
        "    COPY employee_data(education, joining_year, city, payment_tier, age, gender, ever_benched, experience_in_current_domain, leave_or_not)\n",
        "    FROM '/content/Employee.csv'\n",
        "    DELIMITER ',' CSV HEADER\n",
        "  }\n",
        "\"\"\").df()"
      ],
      "metadata": {
        "id": "xcnQHGN31b7z",
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          "base_uri": "https://localhost:8080/",
          "height": 80
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              "\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cursor.query(\"SELECT * FROM postgres_data.employee_data LIMIT 3;\").df()"
      ],
      "metadata": {
        "id": "Ugj7wEsa43-K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "ed775c3b-456f-4e9e-e33b-8797aaafd2d6"
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   leave_or_not  joining_year  payment_tier  age  \\\n",
              "0             0          2017             3   34   \n",
              "1             1          2013             1   28   \n",
              "2             0          2014             3   38   \n",
              "\n",
              "   experience_in_current_domain  gender       city ever_benched  education  \n",
              "0                             0    Male  Bangalore           No  Bachelors  \n",
              "1                             3  Female       Pune           No  Bachelors  \n",
              "2                             2  Female  New Delhi           No  Bachelors  "
            ],
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              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4d19bead-e644-44f7-9f4a-0ec081f3e03d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train the prediction Model\n",
        "Train the XGBoost AutoML model for classification using the `accuracy` metric"
      ],
      "metadata": {
        "id": "1AtF1AtT4OC0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cursor.query(\"\"\"\n",
        "  CREATE FUNCTION IF NOT EXISTS PredictEmployee FROM\n",
        "    ( SELECT payment_tier, age, gender, experience_in_current_domain, leave_or_not FROM postgres_data.employee_data )\n",
        "    TYPE XGBoost\n",
        "    PREDICT 'leave_or_not'\n",
        "    TIME_LIMIT 180\n",
        "    METRIC 'accuracy'\n",
        "    TASK 'classification';\n",
        "\"\"\").df()"
      ],
      "metadata": {
        "id": "NUbo47cG33cp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c8d957d3-7e48-47a1-f48a-ae8f285c3ada"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[flaml.automl.logger: 10-27 05:17:19] {1679} INFO - task = classification\n",
            "[flaml.automl.logger: 10-27 05:17:19] {1690} INFO - Evaluation method: cv\n",
            "[flaml.automl.logger: 10-27 05:17:19] {1788} INFO - Minimizing error metric: 1-accuracy\n",
            "[flaml.automl.logger: 10-27 05:17:19] {1900} INFO - List of ML learners in AutoML Run: ['xgboost']\n",
            "[flaml.automl.logger: 10-27 05:17:19] {2218} INFO - iteration 0, current learner xgboost\n",
            "[flaml.automl.logger: 10-27 05:17:19] {2344} INFO - Estimated sufficient time budget=1155s. Estimated necessary time budget=1s.\n",
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            "[flaml.automl.logger: 10-27 05:20:19] {2630} INFO - retrained model: XGBClassifier(base_score=None, booster=None, callbacks=[],\n",
            "              colsample_bylevel=0.9903174356318001, colsample_bynode=None,\n",
            "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, grow_policy='lossguide', importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=0.3722679382084117,\n",
            "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
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            "              min_child_weight=0.07291503794199583, missing=nan,\n",
            "              monotone_constraints=None, multi_strategy=None, n_estimators=18,\n",
            "              n_jobs=-1, num_parallel_tree=None, random_state=None, ...)\n",
            "[flaml.automl.logger: 10-27 05:20:19] {1930} INFO - fit succeeded\n",
            "[flaml.automl.logger: 10-27 05:20:19] {1931} INFO - Time taken to find the best model: 178.4095482826233\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 0\n",
              "0  Function PredictEmployee added to the database."
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    {
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      "source": [
        "### Utilizing the Prediction Model\n",
        "Following the model training, we proceed to employ the `PredictEmployee` model to make predictions for whether the employee will leave or not."
      ],
      "metadata": {
        "id": "Fc2eLIEB61Pj"
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    {
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        "cursor.query(\"SELECT PredictEmployee(payment_tier, age, gender, experience_in_current_domain, leave_or_not) FROM postgres_data.employee_data LIMIT 10;\").df()"
      ],
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      "execution_count": 11,
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        {
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            "text/plain": [
              "   leave_or_not\n",
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          "metadata": {},
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    {
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        "  SELECT leave_or_not, predicted_leave_or_not FROM postgres_data.employee_data\n",
        "  JOIN LATERAL PredictEmployee(*) AS Predicted(predicted_leave_or_not) LIMIT 10;\n",
        "\"\"\").df()"
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              "   leave_or_not  predicted_leave_or_not\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-24bc3c79-28d4-4d76-88f8-e1a79ce81eb9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-24bc3c79-28d4-4d76-88f8-e1a79ce81eb9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-09e11e00-0a97-484b-959d-086b36e41c2e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-09e11e00-0a97-484b-959d-086b36e41c2e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-09e11e00-0a97-484b-959d-086b36e41c2e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    }
  ]
}